Constellation Plots: Neocortex, 2nd Trimester

All Samples, All cell types

LOAD DATA.

______________________________________________________________________________

• Neocortex_v3.RData Not necessary if just plotting on already calculated PCA, UMAP, clusters.

# Full dataset:
load("../data/cfc4b2_neocortex_v3.RData")

# 40k random subset (toy dataset)
# ncx.40k <- read_rds("constellation_plots/ncx_v3_40k.rds")

# 100k random subset of excitatory neuron lineage only.
ncx.exn.100k <- read_rds("../data/exn_lineage/toy/ncx.v3.exn.sub_100k.rds")

• Cluster / marker tables.

********************************************************************

1. START scrattch.hicat functions to build constellation plots.

********************************************************************

1. Define cells to build the plot from.

[All cells: 404.2K]

a. All clusters / cell types:

Keep only cells with combo2 $combined.cluster.2 annotation.

b. Excitatory neuron lineage only:

Keep only cells with combo2 $combined.cluster.2 annotation AND whose $combined.cluster.2 annotation belongs to excitatory neuronal lineage classes.

b.1. Cells in exn linage 100k cell toy object:

2. Build dataframes for constellation plots.

1. cl

2. rd.dat

3. cl.df

4. rd.cl.center Find cluster centers from UMAP coordinates

5. cl.center.df

Join cl.df and rd.cl.center into cl.center.df for input into get_KNN_graph.

6. knn.cl

3. Make constellation plot

——————————————————————————

2. Find DE genes between connected clusters.

——————————————————————————

Neuron_3 cells with nearest neighbors in Neuron_8

Compare cells above and below the median count of neuron_8 neighbors.

cells <- list()
cells$above.median <- x %>% filter(neuron_8 > median) %>% pull(cell.name)
cells$below.median <- x %>% filter(neuron_8 < median)  %>% pull(cell.name)

s.obj <- SetIdent(Neocortex, cells = cells$above.median, value = 'nn_above_med') %>% 
          SetIdent(cells = cells$below.median, value = 'nn_below_med')

markers <- list()
markers$nn_above_med <- FindMarkers(s.obj, slot = "scale.data", 
                                  # cells.1 = cells$above.median, cells.2 = cells$below.median,
                                  ident.1 = "nn_above_med", ident.2 = "nn_below_med", 
                                  logfc.threshold = 0)

Calculate enrichment ratio, filter genes w. adj p-value < 0.05, sort table. Positive values indicate that the feature is more highly expressed in the first group.

markers.tmp <- markers$nn_above_med %>% rownames_to_column("gene") %>%
  mutate(enrich.ratio = pct.1 / pct.2,
         gene.score = avg_diff * enrich.ratio,
         across(.cols = where(is.numeric), .fns = round, digits = 5)
  ) %>%
  filter(p_val_adj <= 0.05) %>% 
  select(gene, pct.1, pct.2, enrich.ratio, avg_diff, gene.score, p_val_adj) %>%
  arrange(desc(gene.score))

write_tsv(markers.tmp, "../out/DEgenes_neuron3_vs_neuron8.tsv")

Make heatmap:

Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
Specifying width/height in layout() is now deprecated.
Please specify in ggplotly() or plot_ly()